Methoden zur medizinischen Datenmodellierung
Georg Dorffner
Section for Artificial Intelligence, Center for Medical Statistics, Informatics and Intelligent Systems
Medical University of Vienna
Gastpräsentation: Matthias Samwald
Genomic CDS ontology and the
Medicine Safety CodeA technology stack for anchoring personalized
medicine into clinical practice
Mag. Dr. Matthias Samwald Medical University of Vienna
WHAT IS PERSONALIZED MEDICINE / STRATIFIED MEDICINE?
Background
Drug efficacy and toxicity can vary drastically between patients with different genetic profiles
Significant cause of morbidity and mortality!One reason why many promising therapeutics in development fail to reach patients!
Pharmacogenetic assays and treatment algorithms are becoming more and more numerous
Pharmacogenetic assays and treatment algorithms are becoming more and more numerous
Pharmacogenetic assays and treatment algorithms are becoming more and more numerous
Sequencing-based:PGRNseq
…
Microarray-based:23andMe
Affymetrix DMET chipFlorida/Stanford chip
…
CAPTURING PHARMACOGENOMIC DOMAIN KNOWLEDGE IN COMPUTABLE FORM
The Genomic CDS ontology
Pharmacogenetic assays and treatment algorithms are becoming more and more numerous
Sequencing-based:PGRNseq
…
Microarray-based:23andMe
Affymetrix DMET chipFlorida/Stanford chip
…
We are creating an ontology-based framework for representing pharmacogenomic knowledge
and providing clinical decision support
Alleles / haplotypes are gene variants having certainvariants (‚mutations‘) at certain characters in theirgenetic codes
This is how it actually looks in the ontology
1 Class: 'human with CYP2C9*3'
2 EquivalentTo:
3 has some rs1057910_C
4 SubClassOf:
5 has some 'CYP2C9 *3',
6 (has some rs1057910_C) and
7 (has some rs1057911_A) and
8 (has some rs1799853_C) and
9 (has some rs2256871_A) and
10 (has some rs28371685_C) and
11 (has some rs72558188_AGAAATGGAA) and
12 (has some rs72558189_G) and
13 (has some rs9332239_C)
...
This is how it actually looks in the ontology
1 Class: 'human with CYP2C9*18'
2 EquivalentTo:
3 (has some rs1057910_C) and
4 (has some rs1057911_T) and
5 (has some rs72558193_C)
6 SubClassOf:
7 has some 'CYP2C9 *18',
8 (has some rs1057910_C) and
9 (has some rs1057911_T) and
10 (has some rs1799853_C) and
11 (has some rs2256871_A) and
12 (has some rs28371685_C) and
...
Dosing guideline from an FDA drug label
Dosing guideline from an FDA drug label
1 Class: 'human triggering CDS rule 9'
2 Annotations:
3 CDS_message "0.5-2 mg warfarin per day should be considered
4 as a starting dose range for a patient with this genotype
5 according to the warfarin drug label."
6 EquivalentTo:
7 (has some 'CYP2C9 *1') and
8 (has some 'CYP2C9 *3') and
9 (has exactly 2 rs9923231_T)
Describing an individual patient in OWL
1 Individual: example_patient2 Types:3 human,4 (has some rs1208_A) and (has some rs1208_G),5 (has some rs8192709_C) and (has some rs8192709_T),6 (has some rs9934438_A) and (has some rs9934438_G),7 has exactly 2 rs10264272_C,8 has exactly 2 rs9923231_T,9 has exactly 2 rs12720461_C,10 (has some ‘CYP2C9 *1’) and (has some ‘CYP2C9 *3’),11 has exactly 2 ‘CYP2C19 *1’,12 (has exactly 3 CYP2D6) and (has exactly 2 ‘CYP2D6 *1’) 13 and (has exactly 1 ‘CYP2D6 *2’)
...
heterozygous SNP variants
homozygous SNP variants
allelic variantsand copy num-ber variations
Describing an individual patient in OWL
1 Individual: example_patient2 Types:3 human,4 (has some rs1208_A) and (has some rs1208_G),5 (has some rs8192709_C) and (has some rs8192709_T),6 (has some rs9934438_A) and (has some rs9934438_G),7 has exactly 2 rs10264272_C,8 has exactly 2 rs9923231_T,9 has exactly 2 rs12720461_C,10 (has some ‘CYP2C9 *1’) and (has some ‘CYP2C9 *3’),11 has exactly 2 ‘CYP2C19 *1’,12 (has exactly 3 CYP2D6) and (has exactly 2 ‘CYP2D6 *1’) 13 and (has exactly 1 ‘CYP2D6 *2’)
...
heterozygous SNP variants
homozygous SNP variants
allelic variantsand copy num-ber variations
"0.5 - 2 mg warfarin per day should be considered as a starting dose range for a patient with this genotype according to the warfarin drug label."
OWL Reasoner
BUT HOW CAN PERSONALIZED MEDICINE BE PUT INTO PRACTICE?
The Medicine Safety Code initiative
We are creating a barrier‐free system for storing and interpreting personal pharmacogenetic information (based on 2D barcodes and web‐based decision support)
We are creating a barrier‐free system for storing and interpreting personal pharmacogenetic information (based on 2D barcodes and web‐based decision support)
Alternatively, data can also be entered manually
Matching treatment recommendations are displayed
Take‐home messages
• Genomic CDS ontology + Medicine Safety Code system provide a comprehensive solution for clinical pharmacogenetics
• 2D barcodes can be used to quickly provide genetic data (or other patient data) at the point‐of‐care
• RDF/OWL 2 is capabale of providing both decision support functionality as well as flexible knowledge bases for personalized medicine
Thanks!
Local team:
Jose Antonio Miñarro Gimenez
W3C partners:
Richard Boyce (University of Pittsburgh)
Robert R. Freimuth (Mayo Clinic)
Michel Dumontier (Carleton University)
Simon Lin (Marshfield Clinic)
Robert L. Powers (Predictive Medicine, Inc.)
Joanne S. Luciano (Rensselaer Polytechnic Institute)
Eric Prud’hommeaux (W3C)
M. Scott Marshall (MAASTRO Clinic)
Funding:
Austrian Science Fund (FWF): [PP 25608‐N15]
Links:
http://www.genomic‐cds.org/
http://safety‐code.org/
http://samwald.info/
Beispiel 2: Kontinuierliche Schlafmodellierung
Roman Rosipal, Achim Lewandowski, GD
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Continuous sleep profile
Automatic sleep analysis
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Continuous model
Continuous probability vectors
Preprocessing and feature extraction
Supervised learning of class-conditional GMMs
Unsupervised reshuffling of GMMs
AR(10) coefficients
GMMs
Calculate posteriors
GMMs
Polysomnographic recordings
Single channel EEG data
R&K labels
Spindle
Process
Artifacts
Detection
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b003302
0 50 100 150 200 250 300 350 400 4500
1
tim e [m in]
deep
0
1
s2
0
1
s1
0
1
wak
e
C4
C3
otherswake
s1s2s3s4
REM
B003302: Female, 76 years
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“Subjective sleep quality” versus “Objective sleep quality”R&K
SSA-1 Number of stage shifts (/hr TST)
-10.00 0.00 10.00
s_qua_21
-30.00
-20.00
-10.00
0.00
10.00
20.00
fsts
t00c
fstst00c = -0.42 + 0.53 * s_qua_21R-Square = 0.19
20-39 40-59 >=60
-10.00 0.00 10.00
s_qua_21
fstst00c = -1.30 + 0.21 * s_qua_21R-Square = 0.03
-10.00 0.00 10.00
s_qua_21
fstst00c = -1.53 + 0.01 * s_qua_21R-Square = 0.00
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“Subjective sleep quality” versus “Objective sleep quality”hGMM
SSA-1 Number of stage shifts “deep – S2”
-10.00 0.00 10.00
s_qua_21
-0.0200
0.0000
0.0200
0.0400
sc_d
_s2c
sc_d_s2c = 0.00 + -0.00 * s_qua_21R-Square = 0.15
20-39 40-59 >=60
-10.00 0.00 10.00
s_qua_21
sc_d_s2c = 0.00 + -0.00 * s_qua_21R-Square = 0.14
-10.00 0.00 10.00
s_qua_21
sc_d_s2c = 0.00 + -0.00 * s_qua_21R-Square = 0.13
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Result• Measures for sleep continuity and
architecture based on R&K showed significant correlations with subjective sleep quality only in young subjects.
• In contrast, measures for sleep continuity and architecture based on hGMM showed significant correlations in all age-groups
© Alle Rechte liegen bei den Autoren.
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Continuous probability model II
• z: state• x: AR(10) vector• c: RK class• s: spindle class
Model assumption: given the state z, x,c, and s are independent
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Applying the modelExpress the current sleep as R&K posterior for given x and s
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Applying the modelExpress the current sleep as ‚raw‘ state posterior for given x and s
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Relative time spent in a state
N
i ii NaxzpzRTS1
/),|()(
• subject with observations (x1,a1),...,(XN,aN)
• Looking for a measure judging the time spent in a state z, weighted by ‚intensity‘ of a visit:
e.g. calculate sum of posteriors for each state z, relative to length of recording
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Correlation to outside criteria• can compare given sleep quality
variable (e.g. result of concentration test) with RTS(z) for a list of subjects
=> use Spearman-Rank correlation to detect monotonic relationships
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Results: sleep stages
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Results: probabilistic sleep model
Rosipal et al., Biol Psychol, 2013
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HMM trajectories
Same subject(1st/2nd night)
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Individual differences
• Q: Is it really justified to ask for more correlations?
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Conclusions• PSG/EEG objectively describes
physiology of sleep• Visual approaches lead to „fuzzy“
ground truth, automation leads to reliability
• Data-based approaches can extract more information
• But relationship to outside criteria about sleep quality due to other effects (context, individual characteristics
Beispiel 3: Vorhersage der Mortalität nach Herzstillstand
Fritz Sterz, Stefan Aschauer, GD
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me out of hospital cardiac arrest
(OOHCA)
• major health problem
• 500.000 patients in United States and Europe /year
• overall mortality: 8% - 11%
background
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background• OOHCA has a very uncertain
outcome
• no valid outcome scoring system• problem in giving reliable outcome
estimation • delicate decisions
based on experience and gut feeling
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aim
• to assess the predictability of outcome after OOHCA, based on a number of observational variables
• to identify variables with high predictive power
• to assess whether a multivariateapproach is superior to a univariateone
• to derive a OOHCA outcomeprediction score tool
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benefit• improvement of the predictability of
patient’s survival would be of major medical and socioeconomic interest.
• valid outcome estimation could facilitate decision-making for persons in authority and could save medical resources
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methods
• based on a cardiac arrest-registry with > 4000 patients which were resuscitated from OOHCA and which were admitted to the Department of Emergency Medicine at a large University Hospital
• multivariate logistic regression was applied on 20 variables before ROSC deemed to have high predictive power
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methods
• the framework of machine learning was chosen
• a 10-fold cross-validation was done for reliable estimates and confidence intervals
• main performance parameter was the area under the ROC curve (AUC)
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variablesVariable name Description Value Scalesex Sex of the patient Male=0, Female=1 binaryage Age of the patient In years, at the time of cardiac arrest metricbmi Body Mass Index Weight (kg) / Size (m) Squared metricdiabetes Previous diagnosis of diabetes Diabetes = 1, no diabetes = 0 binarysmoker Patient is a smoker Smoker=1, nonsmoker=0 binarymyocinfarct Patient previously had a myocardial infarction Infarction=1, no infarction=0 binarykhk Previous diagnosis of Coronary Artery Disease CAD=1, no CAD=0 binaryhypertension Previous diagnosis of hypertension Hypertension=1, no hypertension=0 binaryheartfail Previous diagnosis of heart failure Heart failure=1, no heart failure=0 binarycvi Previous diagnosis of chronic venous insufficiency CVI=1, no CVI=0 binary
copd Previous diagnosis of chronic obstructive pulmonary disease COPD=1, no COPD=0 binaryopcpre OPC score prior to cardiac arrest Score 1 to 5 ordinal, treated as metricnyh5pre NYH5 score prior to cardiac arrest Score 1 to 5 ordinal, treated as metricnoflow Minutes between cardiac arrest and first aid (length of "no
flow" time) in minutes metricmin2srosc Minutes between cardiac arrest and SROSC in minutes metriccause Main cause of cardiac arrest Cardiac=1, non-cardicac=0 binary
firstaidFirst aid performed by physician, family member, paramedic or layman Physician=1, non-Physician=0 binary
nodefi Number of defibrillation shots Count of shots metricadrenaline Amount of adrenaline applied Total amount (in …) metric
shockable Shockability of rhythm in first defibrillation Shockable=1, non-shockable=0 binarydefireaction Reaction tp the first defibrillation Not shockable=0, shockable and VT/VF (as reaction
to first defi)=1, shockable and PEA=2, shockable+Asystole=3, shockable+SR/RHY/SVES/VES/AVES+ no pulse=4, shockable+pulse=5
ordinal, treated as metric
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Initial variables, histograms
0 0.5 10
500
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0 0.5 10
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-5 0 50
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-5 0 50
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-10 0 100
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-5 0 50
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-10 0 100
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Data sets
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witnessed Trainingset median 25% percentile
75% percentile Percent 1 Percent 0
sex 27.53% 72.47%age 59 49 69 0 0bmi 26.12 23.88 29.22 0 0diabetes 16.20% 83.80%smoker 30.90% 69.10%myocinfarct 12.92% 87.08%cad 21.91% 78.09%hypertension 32.21% 67.79%heartfail 11.05% 88.95%cvi 5.99% 94.01%copd 9.74% 90.26%opcpre 1 1 1 nyh5pre 1 1 2 noflow 1 0 6.5 min2srosc 20 10 30 cause 69.76% 30.24%firstaid 34.18% 65.82%nodefi 2 0 4 adrenaline 2 0 4 defireaction 1 0 2 shockable 59.83% 40.17%cpc30d 3 1 5 mortality 39.89% 60.11%
Testset median 25% percentile
75% percentile Percent 1 Percent 0
sex 27.84% 72.17%age 61 50 71 bmi 26.23 24.11 29.41 diabetes 20.62% 79.38%smoker 31.62% 68.39%myocinfarct 14.09% 85.91%cad 24.74% 75.26%hypertension 41.92% 58.08%heartfail 14.78% 85.22%cvi 4.81% 95.19%copd 6.53% 93.47%opcpre 1 1 2 nyh5pre 1 1 2 noflow 1 0 5 min2srosc 19 12 32 cause 62.54% 37.46%firstaid 49.49% 50.52%nodefi 1 0 3 adrenaline 1 0 3 defireaction 1 0 2 shockable 54.30% 45.70%cpc30d 3 1 5 mortality 42.27% 57.73%
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Non-witnessedTrainingset median 25% percentile 75% percentile Percent 1 Percent 0 sex 30.46% 69.54%age 56 41 68 bmi 25.94 22.86 28.72 diabetes 14.37% 85.63%smoker 27.01% 72.99%myocinfarct 9.77% 90.23%khk 16.09% 83.91%hypertension 26.44% 73.56%heartfail 10.92% 89.08%cvi 4.02% 95.98%copd 8.62% 91.38%opcpre 1 1 1 nyh5pre 1 1 1 cause 43.68% 56.32%nodefi 1 0 4 adrenaline 4 2 6.5 defireaction 0 0 1 shockable 36.78% 63.22%cpc30d 5 5 5 mortality 76.44% 23.56%Testset median 25% percentile 75% percentile Percent 1 Percent 0 sex 20.00% 80.00%age 54 44.5 64.25 bmi 26.23 23.32 28.18 diabetes 0.00% 100.00%smoker 32.00% 68.00%myocinfarct 4.00% 96.00%khk 12.00% 88.00%hypertension 44.00% 56.00%heartfail 4.00% 96.00%cvi 4.00% 96.00%copd 16.00% 84.00%opcpre 1 1 1 nyh5pre 1 1 1 cause 60.00% 40.00%nodefi 1 0 6.25 adrenaline 4 2 8 defireaction 1 0 1 shockable 60.00% 40.00%cpc30d 5 1.75 5 mortality 68.00% 32.00%
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Score
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Simplifed scorePredictor Points Predictor Points
1. Age group 3. Minutes until SROSC>80 32 >100min 35>70 27 >50min 21>50 23 >40min 13>60 20 >30min 10>40 16 >20min 7≤40 11 >10min 4
>0min 12. Adrenalin administered 0min 0>10mg 24>5mg 12 4. Shockable rhythm?>4mg 7 Yes ‐15>3mg 5 No 0>2mg 4>1mg 2>0mg 10mg 0 Total score
Total score Probability for mortality
<13 10%13‐22 30%23‐30 50%31‐40 70%>40 90%
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predicted mortality
Beispiel 4: Simulation molekularer Dynamiken
Bernhard Knapp, Wolfgang Schreiner, GD
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Molecular dynamics simulation –T-cells
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Patterns within 50ns
Knapp et al., Plos One, 2013
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Statistical significance
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Affected regions